Many digital imaging systems include three main components: a mechanism for generating the source digital imagery, a mechanism for processing the digital image data, and a mechanism for visualizing the imagery. As such, many digital imaging systems employ more than one image processing method, or algorithm, designed to enhance the visual quality of the final rendered output. In particular, image processing methods of interest are methods for adjusting the overall balance, or brightness of digital images.
In the journal article Automatic Color Printing Techniques published in Image Technology, April/May 1969,the authors Hughes and Bowker describe an automatic method of printing color negative film onto photographic paper. In this article, Hughes et al. compare their method to the predominant method of the time, namely large area transmission density (LATD) The LATD method, which involves sensing the color of the overall film negative, is described as failing to accurately predict the color balance for natural scenes which are dominated by a single color The LATD measurements are reliable only when the scene is composed of a random sampling of red, green and blue objects. The new method described by Hughes et al. involved the steps of scanning the film negative with a red, green, and blue sensitive line scanner capable of resolving reasonable spatial detail, developing two color-difference signals by subtracting the green signal from the red signal and the blue signal from the red signal, forming a spatial derivative of the color-difference signals, calculating an average color balance for the film negative by rejecting image region not exhibiting color activity, and exposing the film negative onto photographic paper using the calculated color balance to adjust the overall color of the print. The differentiation operation employed by Hughes and Bowker involves the calculation of subtracting adjacent signal values i.e. forming a gradient signal. Hughes and Bowker identified a link between regions of images which exhibit spatial activity and the likelihood of those image regions as being good estimates of color balance.
The minimum total variance (MTV) filter and the median absolute difference (MAD) filter described by P. A. Dondes and A. Rosenfeld in the journal article Pixel Classification Based on Gray Level and Local “Busyness” in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-4, No 1, pp. 79-84, 1982,is an example of a non-linear digital image processing filter which produces a metric, or value, relating to spatial activity. In this article, Dondes and Rosenfeld disclose two spatial activity algorithms based on calculating the vertical and horizontal gradient signals, or pixel differences, of a digital image. For a 3 by 3 pixel region centered about a pixel of interest e,                a b c        d e f        g h ithe MTV can be calculated as the minimum of the sum of the horizontal gradient magnitudes and the sum of the vertical gradient magnitudes, orMTV=min(|a−b|+|b+c|+|d−e|+|e−f|+|g−h|+|h−i|, |a−d|+|d−g|+|b−e|+|e−h|+|c−f|+|f−i|)The MAD value is given by the median value of the 12 gradient magnitudes asMAD=median(|a−b|,|b−c|, |d−e|, |e−f|, |g−h|, |h−i|, |a−d|, |d−g|, |b−e|, |e−h|, |c−f|, |f−i|).Both the MTV and MAD filters are calculated for each pixel in the digital image thus producing a digital image whose value at a given pixel location is a measure of the spatial activity of the original image about the same pixel location. These filters were intended as digital image processing pixel classification filters.        
Dinstein et al. described the MAXDIF filter as a spatial activity metric in their journal article Fast Discrimination Between Homogeneous and Textured Regions published in Seventh International Conference on Pattern Recognition, Montreal, Canada, Jul. 30-Aug. 2, 1984,pp. 361-363. The MAXDIF filter involves calculating the minimum and maximum pixel value within local neighborhood of pixels about a pixel of interest. For a 3 by 3 pixel region centered about a pixel of interest e,                a b c        d e f        g h ithe output of the filter is given byMAXDIF=max(a,b,c,d,e,f,g,h,i)−min(a,b,c,d,e,f,g,h,i)        
The MAXDIF filter described by Dinstein et al. is calculated for each pixel in the digital image thus producing a digital image whose value at a given pixel location is a measure of the spatial activity of the original image about the same pixel location. This filter was intended as digital image processing pixel classification filter.
In U.S. Pat. No. 5,016,043 issued May 14, 1991, Kraft et al. disclose a method of color balancing and brightness balancing for photographic optical printers. In the disclosure, photographic film negative originals are scanned photoelectrically by regions and three color densities are determined for each scanning region. Each scanning region has multiple photoelectric response values produced with a high resolution scanning system. A detail contrast parameter describing the detail contrast in the scanning region is calculated by finding the maximum and minimum values taken from the multiple photoelectric response values. The detail contrast parameters for each of the scanning regions are evaluated together with the color densities of the scanning regions for the determination of the exposure light quantities. These exposure values are used to control the amount of light passing through the photographic film negative onto photographic paper and relate to the average density of the photographic film sample. In particular, in the correction of densities, scanning regions with higher detail contrasts are considered stronger than those with lower density contrasts, while color corrections are carried out in exactly the opposite manner.
The optical printing exposure method disclosed by Kraft et al. in U.S. Pat. No. 5,016,043 uses the same principle of spatial activity described by Hughes and Bowker for determining color balance. Kraft et. al extends the concept to include the determination of brightness balance based on spatial activity. While the method disclosed by Kraft et al. is useful for adjusting the overall brightness and color balance of an optical print, the technology cannot be used directly for adjusting the brightness tone of a digital image. In the method described by Kraft et al. only a low resolution sampled version of photographic film negative is ever available for computation. Thus no digital image is formed by the method disclosed Furthermore, no method for adjusting the brightness tone of digital image is mentioned. However, the computed average density of the photographic film sample does relate to the desired overall brightness tone adjustment of a digital image.
In U.S. Pat. No. 4,984,013 issued Jan. 8, 1991, Terashita describes a method of calculating the amount of exposing light for a color photographic film negative involving the steps scanning the original negative in red, green, and blue color sensitivities photoelectrically, calculating color density difference values for the red, green and blue signals of adjacent pixel values, comparing the color density difference values to a threshold value, classifying the pixels as either belonging to subject or background regions based on color difference values, calculating a printing exposure based on a statistical quantity sampled from the subject region of pixels. Alternately, the method describes the use of a color chrominance signal for forming the color density difference values. The method described by Terashita builds on the principles described by Hughes and Bowker by extending the idea of using spatial derivatives to calculate brightness balance for printing exposure control. However, the method described by Terashita does not teach a method for adjusting the brightness of a digital image Furthermore, the formulation of the pixel classification on the basis of color and/or chrominance signals rather than luminance signals makes the method more susceptible to noise.
U.S. Pat. No. 5,724,456 issued Mar. 3, 1998 to Boyack et al., describes a method for processing a digital image signal designed to adjust the tonal brightness and contrast characteristics of the digital image In this method, the luminance values versus a tonal reproduction capability of a destination application are used. The system includes a device for partitioning the image into blocks, then combining certain blocks into sectors. An average luminance block value is determined for each block and a difference is determined between the maximum and minimum average luminance block values for each sector. If the difference exceeds a predetermined threshold value, then the sector is labeled as an active sector and an average luminance sector value is obtained from maximum and minimum average luminance block values. All weighted counts of active sectors of the image are plotted versus the average luminance sector values in a histogram, then the histogram is shifted via some predetermined criterion so that the average luminance sector values of interest will fall within a destination window corresponding to the tonal reproduction capability of a destination application. The method described in this patent adjusts digital image tone scale characteristics, i.e. the brightness of different image regions is affected differently. The method taught in U.S. Pat. No. 5,724,456 does not adjust the overall digital image brightness.
U.S. Pat. No. 4,394,078 issued Jul. 19, 1983 to Terashita, describes an exposure control method for a camera based on the scene brightness measured by the use of a number of light measuring elements located at positions to receive light from the scene. The light receiving area is divided into several zones. In each of the zones, at least one light measuring element is provided to measure the brightness of the scene in each zone and is used to give the maximum or minimum brightness in each zone. Exposure is controlled based on a weighted mean value. Also described is an exposure control method based on another brightness calculation involving the mean value of the outputs of all the light measuring elements.
The method taught by J. Hughes and J. K. Bowker forms the basis of methods taught by Kraft et al. and Terashita, i e formulating a gradient signal (or pixel difference signal) as a measure of spatial activity, comparing the gradient signal to a predetermined threshold to reject some portions of the image (classify the image pixels), and balancing the image by deriving a numerical average of the signals from the selected portion of the image. The methods described by Hughes et al., Kraft et al, and Terashita all relate to optical printer exposure control and do not teach a method of setting camera exposure or adjusting the brightness of a digital image. The methods taught by Kraft et al, and Terashita create binary masks which can produce statistically unstable results by virtue of the on/off nature of the inclusion/exclusion logic. Furthermore, the spatial activity measure used by Terashita is not isotropic (sensed equally in all directions) and therefor less robust than the isotropic measures of spatial activity described by Dondes et al. What is needed is robust measure of spatial activity used for adjusting the brightness of a digital image, setting the exposure for a photographic camera, and optical printer.